Linkage Of GTAP And DRAM For Scenario Assessment .
Linkage of GTAP and DRAM for scenario assessment: methodology,application and some selected resultsJohn Helming, Andrzej Tabeau, Tom Kuhlman and Frank van Tongeren1.IntroductionA model that is fully consistent on all levels of aggregation from micro to macro isnot available and probably also not feasible. Looking at the agricultural sector, sectormodels give details about this sector, but there is no interaction between theagricultural sector and the rest of the economy. On the other hand, more macrooriented models give too few details for agriculture, especially concerning supplyresponse in the face of sometimes complex policy measures and specific agronomicfeatures. As a result different type of models exists and if there is any overlap betweenthe models they could produce different results for the same variables. To counteractthis problem and to reach results that are more consistent with economic behavior atdifferent levels of aggregation, different models can be linked.The goal of this paper is twofold. First, it describes the technical issues connectedwith linking the Global Trade Analysis Project (GTAP) model and the DutchRegionalized Agricultural Model (DRAM) in one consistent system of models.DRAM is a non-linear, partial equilibrium, positive mathematical programmingmodel of the Dutch agricultural sector. It generates production volume for a numberof crops and animal products as well as (among other outputs) manure at the regionallevel (Helming, 2005). GTAP is a standard comparative static multi-region AppliedGeneral Equilibrium (AGE) model of trade and production at the world level. Inaddition, both models are linked to the Land Use Scanner, which assesses spatialallocation of land for different uses. Since the Land Use Scanner uses a GeographicInformation System (GIS), it enables the generation of spatially disaggregated results.Second, the paper assesses, using the developed model system, the economicconsequences of two contrasting scenarios for food production and naturemanagement in The Netherlands.2.Modeling frameworkThe modeling framework used in this study was based on the GTAP model - themulti-region, multi-sector, computable general equilibrium model – which was usedto access the world wide economic consequences of the scenarios (Section 2.1). TheGTAP model was linked to the DRAM, which is Dutch regional agricultural sectormodel with environmental aspects. The focus of DRAM is on product- as well as andregion-specific production technologies and on the production decisions of farmers(Section 2.2). In addition, both models are linked to the Land Use Scanner toendogenize the agricultural land availability via changes of the asymptote of the landsupply curve (Section 2.3).The models mentioned above are linked in such a way that in the projectiongeneration process output of one of them becomes input for the other. The resultingmodel chain and its use in the prediction process are described in Section 2.4.1
2.1.GTAPThe economic analysis was done with an extended version of the general equilibriummodel of the GTAP model (Hertel, 1997). The standard version of GTAP waschanged to model the specific features of agricultural sector not included in thestandard model version. The extended version of the GTAP (see van Meijl et al. 2006)includes:The new land allocation method that takes into account the variation ofsubstitutability between different types of land use. For this the OECDs PolicyEvaluation Model (see: Huang et al. 2003 and OECD 2003) structure wasused, as it is more detailed and distinguishes different types of land in a nested3-level CET structure.The land supply curve that allows on endogenisation of the land supply. Theland supply curve specifies the relation between land supply and a rental rate(Abler, 2003) and is described by the following equation:Land supply a - b/real land price--2.2.(1)where: “a” is an asymptote interpreted as the maximal potentially availableagricultural land and b is a positive parameter determining the curve.Factor markets segmentation between agriculture and non-agriculture, whichtakes in account both wages and capital return differentials between thesesectors. The segmented factor markets for labor and capital are incorporated inthe standard GTAP model by specifying a CET structure that transformsagricultural labor (and capital) into non-agricultural labor (and capital) (Herteland Keening, 2003). In order to have separate market clearing conditions foragriculture and non-agriculture, labor and capital markets were segmented inthe model with a finite elasticity of transformation and the separate marketprices for each type of labor and capital were introduced.Agricultural production quotas, which places a restriction on the volume ofproduction. If such a supply restriction is binding, it implies that consumerswill pay a higher price than they would pay in case of an unrestricted interplayof demand and supply. A wedge is created between the prices that consumerspay and the marginal cost for the producer. The difference between theconsumer price and the marginal cost is known as the tax equivalent of thequota rent. This is implemented to the model by formulating the quota as acomplementarity problem. This formulation allows for endogenous regimeswitches from a state when the output quota is binding to a state when thequota becomes non-binding. In addition, changes in the value of the quota rentare endogenously determined.DRAMDRAM can be defined as a comparative static, partial equilibrium, mathematicalprogramming, regionalized model of the Dutch agricultural sector with environmentalaspects. It generates production volume for a number of crops and animal products aswell as (among other outputs) manure at the regional level (Helming, 2005). DRAMdistinguishes 14 regions on the basis of agricultural potential.2
The focus of DRAM is on regional and national agricultural production andthe interactions between agricultural activities in terms of agricultural input andoutput markets. DRAM concentrates on the effects of policy changes on inputallocation and prices. The core of DRAM is an optimization block that maximizestotal profits from agriculture with the restriction that economic, technical,environmental, spatial and policy constraints are respected. Here, profits are definedas revenue minus total variable costs. The basic underlying assumption is that farmers'behavior can be described by the maximization of profits from individual agriculturalactivities. Profits are maximized simultaneously across all farms to take into accountthe relationship between market effects and farmers' behavior. Simultaneousoptimization of farm profits assumes an optimal allocation of agricultural inputs andoutputs across the farms, so that profits from agriculture at the national level aremaximized. This optimal allocation of inputs and outputs is achieved when marginalcosts are greater than or equal to marginal revenues for all agricultural activities in themodel.Prices of outputs and purchased variable inputs are treated as exogenousvariables, as they are assumed to be determined at the internal EU market or worldmarket. Fixed inputs in the model are land and quotas.2.3.Land Use ScannerThe Land Use Scanner is a land-use simulation model developed for the Netherlands(Hilferink and Rietveld, 1999). The Land Use Scanner combines land claims withspatial data on existing land use, land suitability and policies into a forecast for futureland use. This forecast is in cells of 500 x 500 meters. Land claims per land-use classare exogenous to the model.The land allocation in cells is grounded in economic theory. The fundamentalhypothesis is that land use is determined by the suitability of land for a particularpurpose. Different land-use categories are pictured as actors competing for limitedspace, with each area of land going to the category that can derive the largest benefitsfrom it – an approach based on the bid-rent theory for urban land use (Alonso 1964)and on von Thünen’s theory of agricultural land use (von Thünen, 1875). This theoryleads to a logit-type land allocation equation. Two constraints are added to thisequation: one to ensure that the total area of land allocated to land use j is equal to thetotal amount needed; that amount (the claim) is derived exogenously. The secondconstraint ensures that the total amount of land allocated to all uses is equal to theamount available.2.4.Model chain in the projection processFigure 1 shows the models use in the prediction process. The models’ chain startsfrom the Land Use Scanner, which calculates the land-use projections being aconsequence of expected economic developments and of government policies on theuse of space and other scenario assumptions. The land-use projections from the LandUse Scanner are fed into GTAP, which assess the consequences of the scenarios forthe Netherlands as a part of the world economy. The land use projections from LandUse Scanner are used to alter the asymptote of the land supply curve in GTAP. Theoutput of the GTAP model includes real product prices and sectoral productivitychanges. They, in turn, are used in DRAM, which generates production volume for a3
number of crops and animal products as well as (among other outputs) manure at theregional level.World Vision(scenarioseconomic policystory lines)global technical progresssocial developmentconsumption patterninternational cooperationEconomic growthPopulation growthsectoral technical progressrealpricesPrices, demand,production andtrade in agricultural productionproducts (GTAP)availableagriculturallandTotal agriculturalland (Land riculturalproduction and landuse (DRAM)Figure 1: The modeling framework of Land Use Scanner, the GTAP model andDRAMSince both the GTAP and DRAM models predict the production changes ofagricultural sectors, the iterative solving procedure of both models leading to theconsistent production results is necessary. The DRAM and the GTAP modelproduction changes can differ. These differences are the result of differences in thecost structures in DRAM and the GTAP model caused, e.g., by manure policy anddifferent product and region specific production technologies taken in to account inDRAM but not present in the GTAP model. The tax or subsidy equivalents of thesecosts will be calculated to fix the sectoral production in the GTAP model on the levelobtained by DRAM. This in turn will produce new real product prices andproductivity changes, which will be used for DRAM simulations to calculate the newoutput changes. The iteration process stops when the agricultural production changesin DRAM will be sufficiently close in the two consecutive iterations.Below it is discussed how consistency between DRAM and GTAP isachieved.4
3.Making DRAM consistent with GTAP: approach useAccording to Jensen et al. (2002) linking different models to analyze specificscenarios require coordination in at least two respects:- consistency in behavioral description in the involved models;- consistency in the definition of the scenarios analyzed.Consistency in the models’ behavioral descriptions assumes that reactions of themodels to exogenous shocks are consistent. This requires that underlying functionalspecifications and behavioral parameters are the same in the involved models. Thismeans for example that elasticity of substitution between different inputs inproduction or different commodities in consumption are identical across models. Inthe case of DRAM and GTAP, the behavior of the two models can be quite different.GTAP is a multi-country general equilibrium model focusing on trade and substitutionbetween fixed resources in production and substitution in consumption. DRAM is apartial equilibrium model focusing on regional supply of key agricultural outputs,taking into account substitution in production through joint use of land and manureapplication room. Moreover, differences in behavior can be expected because ofdifferences in specification of variables, differences in data sources and differences inbase year. Among others these differences explain the differences in cost shares, asdescribed in paragraph 3.1.Consistency in the definition of scenarios requires that the set of variablesexogenous to both models are the same. A complication to that requirement is the factthat some variables may be exogenous in one model but endogenous to anothermodel. For example, manure markets are endogenous in DRAM but they not includedin GTAP. In this case an iterative procedure is necessary to ensure consistencybetween manure prices in DRAM and output prices in GTAP.3.1.Costs shares of different costs components in DRAM and GTAPThis section analyses the differences in costs shares of different cost components inDRAM and GTAP. Table 1 and 2 show the costs shares of feed, other intermediates,internal deliveries, land and other fixed inputs (capital plus labor) in total costs in thebase in GTAP and DRAM respectively. Tables 1 and 2 show that in the grain sectorcosts shares of capital and labor in GTAP exceeds the comparable costs shares inDRAM. This is partly explained by the base difference in the data bases of bothmodels. The GTAP model uses 2001 but DRAM uses 1996 data, which are onlypartly updated to 2002 data. Nevertheless it is surprising that the costs shares ofcapital and labor in the grain sector exceed the costs shares of capital and labor in thesugar sector in GTAP. This is maybe explained by the fact that table 1 uses data fromthe GTAP database for only one year and this could be a relative good year forcereals. A solution to this would be to use average results over a certain time period.The cost structure in the horticultural sector in DRAM and GTAP arerelatively comparable. Costs of seed and other internal deliveries in the crops sectorare included as intermediates in DRAM. Here again costs shares of capital and laborin GTAP exceeds comparable costs shares in DRAM. This is maybe explained by thehigh prices of consumption potatoes in 2001. The livestock sectors also showrelatively large differences in costs shares between DRAM and GTAP. Especially the5
costs share of labor and capital in the cattle sector in GTAP exceeds thecorresponding costs share of labor and capital in DRAM by far.Table 1: Shares of different cost(index)WheatsugarbeetsOtherintermediates tal pluslabor0,470,37Land0,060,05Total1,001,00Source: GTAP database 6.4components per sector in GTAP in base (2001)Table 2: Shares of different cost(index)wheatsugarbeetsOtherintermediates 0,530,43FeedInternaldeliveriesCapital pluslabor0,380,52Land0,080,04Total1,001,00Source: DRAM databasecomponents per sector in DRAM in base (2002)3.2.horticul- cropsturecattlepig and ,041,000,140,021,000,450,061,00horticul- cropsturecattlepig and ,041,00Calibration of DRAM parameters using GTAP resultsDRAM includes flexible functional forms to model supply and demand of agriculturaloutputs. To make the behavior of DRAM and GTAP consistent as much as possible,we calibrate the parameters of DRAM equations using price and quantity pairs anddemand elasticities derived from GTAP.To achieve consistency between GTAP and DRAM for a given scenario,changes of sectoral output prices, sectoral productivity and activity levels from GTAPare used to calibrate the parameters of the activity specific inverse supply equations inDRAM.Prices changes of outputs per sector from GTAP can be directly linked to finaloutput prices changes in DRAM from the linkage of sectors and activities as describedin the Appendix 1). Price changes of roughage and young animals are not availablefrom GTAP therefore related changes in final outputs are used (e.g. prices of piglets6
in DRAM are related to prices of outputs from sector pig and poultry sector inGTAP).Yield changes per hectare in the arable and horticulture sectors are linked tocorresponding activities in DRAM. GTAP does not include animals as an inputcategory. Therefore, productivity changes per animal (e.g. meat per pig, milkproduction per dairy cow) in DRAM are derived from figures found in the literature.GTAP also delivers changes in milk production per hectare. Together with changes inmilk production per dairy cow, changes in number of dairy cows per hectare pertechnology in DRAM are calculated.Production is endogenous in DRAM. Besides scenario specific changes ininput and output prices and productivity, changes in land allocated to crop sectorsfrom GTAP are used to re-calibrate the parameters of the inverse supply equations,such that consistency between prices and quantities between GTAP and DRAM isreached (see Appendix 2). For the animal activities the procedure is slightly different.From GTAP the change in output of the livestock sectors is known. Using theexogenous change in animal productivity, mentioned above, we can calculate thechange in the number of animals.Results from GTAP, are also used to calculate scenario specific priceelasticities of demand per sector. Together with the corresponding price and quantitypairs, these elasticities are used to calibrate the parameters of output and activityspecific total inverse linear demand functions for final agricultural outputs and exportdemand functions for roughage and young animals. The problem is that priceelasticity of demand is an endogenous variable in GTAP. This means that priceelasticities of demand have to be re-calculated in each iteration until convergence isreached, that is price and quantity changes per period are constant in every followingiteration.3.3.Iterative procedureEven when DRAM parameters are calibrated using GTAP data, production changescalculated from DRAM can be (and mostly are) different than these produced byGTAP. The most important reason of this is the inclusion of manure markets inDRAM. These markets are not modeled in GTAP. As the result, consistency betweenproduction changes in GTAP and DRAM, can only be reached by applying aniterative procedure.Graphically the iterative procedure is presented below. Given a certainscenario assumptions, GTAP is solved first. Then prices and quantities taken fromGTAP are translated to corresponding DRAM variables and are used to calibrate theparameters of the supply and demand functions in DRAM (see appendix 2). Next,DRAM is solved. Results differ from GTAP results because of the inclusion ofmanure markets in DRAM and other differences e.g. differences in costs shares. In thenext iteration, changes in output from DRAM are used as exogenous output changesin GTAP. The GTAP is solved and corresponding GTAP changes in prices andelasticities are used as input in DRAM. This iterative procedure continues untilconsistency is reached, that is in every following iteration price and quantity changesper scenario are constant. To illustrate how results from GTAP are used in DRAM,DRAM results of iteration 0 are discussed in Appendix 3 of this paper.7
Pgtap 1SdramSgtapPdram 1Pgtap 0 Pdram 0DdramQdram 0 Qdram 1 Qgtap 0 Qgtap 1Figure 2: Graphical presentation of the iterative procedure between GTAP andDRAM after two iterations.Graphically the approach to converge supply and demand in DRAM and inGTAP is presented in Figure 2. The variables in Figure 2 are defined as follows:Qgtap 0 output from GTAP in iteration 0Qdram 0 output from DRAM in iteration 0Qgtap 1 output from GTAP in iteration 1 (equal to output fromDRAM in iteration 0)Qdram 1 output from DRAM in iteration 1Pgtap 0 market price from GTAP in iteration 0Pdram 0 market price from DRAM in iteration 0 (equal to market pricefrom GTAP in iteration 0)Pgtap 1 market price from GTAP in iteration 1Pdram 1 market price from DRAM in ite
Regionalized Agricultural Model (DRAM) in one consistent system of models. DRAM is a non-linear, partial equilibrium, positive mathematical programming model of the Dutch agricultural sector. It generates production volume for a number of crops and animal products as well as (among oth
with this version, the model used here is derived from the version 6.1 of the GTAP model based on 1997 data (version 5 of the GTAP data base). In addition to inter-fuel substitution, this model incorporates some further improvements, such as the computation
Accessing data stored in the DRAM array requires a DRAM chip to perform a series of fundamental operations: activation, restoration, and precharge.1A memory controller orchestrates each of the DRAM operations while obeying the DRAM timing parameters.
Disusun oleh deretan flip-flop. Baik SRAM maupun DRAM adalah volatile. Sel memori DRAM lebih sederhana dibanding SRAM, karena itu lebih kecil. DRAM lebih rapat (sel lebih kecil lebih banyak sel per satuan luas) dan lebih murah. DRAM memrlukan rangkaian pengosong muatan. DRAM cenderung
2-5 UltraLift Concept 16 3-1 Watt’s Straight Line Mechanism 19 3-2 Fully Prismatic Linkage 19 3-3 One Replace Prismatic 20 3-4 Two Replaced Prismatics 21 3-5 Fully Revolute Linkage 22 3-6 Scissors Linkage 23 3-7 Parallel Linkage 24 3-8 Parallel Linkage with a Cam 24 3-9 Constant orientation linear linkage 25 3-10 Hydraulic Mechanism 25
Handbook of Computable General Equilibrium Modeling . It characterizes GTAP in four different dimensions: institutional innovation, a network, a database and a standardized modeling platform. Guiding principles for the GTAP modeling framework include flexibility, ease of use, transparency, and . students and published in the Cambridge .
This paper provides the first large-scale study of DRAM memory errors in the field. It is based on data collected from Google’s server fleet over a period of more than two years making up many millions of DIMM days. The DRAM in our study covers multiple vendors, DRAM densities and technologies (DDR1, DDR2, and FBDIMM).
estimation, preferably during the design phase, of system power consumption becomes vital. In addition, since the CPU-DRAM subsystem (comprising CPU, DRAM, DRAM controller, and address/data buffers) forms the core of many of today's desktop and portable computers, accuracy in the assessment of the power requirements of this block
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